AI & Technical question
Create the algorithm for ridesharing concept for uber pool. Explain the goals, logic, factors considered and success metrics.
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What this question tests
Technical and algorithmic product thinking, defining the logic and tradeoffs behind a matching algorithm.
How to approach it
- State the goal, matching multiple riders with overlapping routes into a single trip to reduce cost per rider and vehicle usage.
- Define the inputs, each rider's pickup point, destination, and time window, plus real-time driver location and current route.
- Define the core logic, minimize total added detour time and distance for all matched riders while keeping each rider's added time under an acceptable threshold.
- Explain the tradeoff, matching more riders lowers cost per trip but increases individual travel time, so a detour cap balances savings against rider experience.
- Consider fairness and dynamic pricing, ensuring the algorithm does not consistently disadvantage certain routes or times of day.
- Define success metrics, average cost savings per pooled rider, average added detour time, and pool match rate.
What a strong answer includes
- States the actual optimization objective explicitly, minimizing detour while capping added time per rider, not just 'match people going the same way'.
- Names the core tradeoff, cost savings versus rider time, and proposes a concrete mechanism, a detour cap, to balance it.
- Addresses fairness, since a purely cost-optimizing algorithm could systematically give certain riders worse experiences.
- Sets metrics on both sides of the tradeoff, cost savings and added detour time, not just one.
Common mistakes
- Describing the concept of pooling without defining the actual optimization objective or constraints.
- Ignoring the tradeoff between cost savings and individual rider experience.
- Not proposing any fairness safeguard against a purely cost-optimizing algorithm.
Likely follow-up questions
- How would you set the detour time cap.
- How would you handle a rider who is picked up but their match cancels.
- How would you measure whether the algorithm is fair across riders.
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture